import numpy as np from numpy import random import pandas as pd import streamlit as st import pymongo st.set_page_config(layout="wide") @st.cache_resource def init_conn(): uri = st.secrets['mongo_uri'] client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) db = client["NHL_Database"] return db db = init_conn() wrong_acro = ['WSH', 'AZ'] right_acro = ['WAS', 'ARI'] game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'} team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}', '5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.2%}'} player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}', '4x%': '{:.2%}','GPP%': '{:.2%}'} @st.cache_resource(ttl = 599) def player_stat_table(): collection = db["Player_Level_ROO"] cursor = collection.find() load_display = pd.DataFrame(cursor) load_display.replace('', np.nan, inplace=True) player_stats = load_display.copy() dk_load_display = load_display[load_display['Site'] == 'Draftkings'] fd_load_display = load_display[load_display['Site'] == 'Fanduel'] dk_load_display = dk_load_display.sort_values(by='Own', ascending=False) fd_load_display = fd_load_display.sort_values(by='Own', ascending=False) dk_load_display = dk_load_display.dropna(subset=['Own']) fd_load_display = fd_load_display.dropna(subset=['Own']) dk_roo_raw = dk_load_display fd_roo_raw = fd_load_display return player_stats, dk_roo_raw, fd_roo_raw @st.cache_data def convert_df_to_csv(df): return df.to_csv().encode('utf-8') player_stats, dk_roo_raw, fd_roo_raw = player_stat_table() opp_dict = dict(zip(dk_roo_raw.Team, dk_roo_raw.Opp)) t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" tab1, tab2 = st.tabs(['Pivot Finder', 'Uploads and Info']) with tab1: col1, col2 = st.columns([1, 5]) with col1: st.info(t_stamp) if st.button("Load/Reset Data", key='reset1'): st.cache_data.clear() for key in st.session_state.keys(): del st.session_state[key] player_stats, dk_roo_raw, fd_roo_raw = player_stat_table() opp_dict = dict(zip(dk_roo_raw.Team, dk_roo_raw.Opp)) t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" data_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='data_var1') site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1') if site_var1 == 'Draftkings': if data_var1 == 'User': raw_baselines = proj_dataframe elif data_var1 != 'User': raw_baselines = dk_roo_raw[dk_roo_raw['Slate'] == 'Main Slate'] raw_baselines = raw_baselines.sort_values(by='Own', ascending=False) elif site_var1 == 'Fanduel': if data_var1 == 'User': raw_baselines = proj_dataframe elif data_var1 != 'User': raw_baselines = fd_roo_raw[fd_roo_raw['Slate'] == 'Main Slate'] raw_baselines = raw_baselines.sort_values(by='Own', ascending=False) check_seq = st.radio("Do you want to check a single player or the top 10 in ownership?", ('Single Player', 'Top X Owned'), key='check_seq') if check_seq == 'Single Player': player_check = st.selectbox('Select player to create comps', options = raw_baselines['Player'].unique(), key='dk_player') elif check_seq == 'Top X Owned': top_x_var = st.number_input('How many players would you like to check?', min_value = 1, max_value = 10, value = 5, step = 1) Salary_var = st.number_input('Acceptable +/- Salary range', min_value = 0, max_value = 1000, value = 300, step = 100) Median_var = st.number_input('Acceptable +/- Median range', min_value = 0, max_value = 10, value = 3, step = 1) pos_var1 = st.radio("Compare to all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_var1') if pos_var1 == 'Specific Positions': pos_var_list = st.multiselect('Which positions would you like to include?', options = raw_baselines['Position'].unique(), key='pos_var_list') elif pos_var1 == 'All Positions': pos_var_list = raw_baselines.Position.values.tolist() split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1') if split_var1 == 'Specific Games': team_var1 = st.multiselect('Which teams would you like to include?', options = raw_baselines['Team'].unique(), key='team_var1') elif split_var1 == 'Full Slate Run': team_var1 = raw_baselines.Team.values.tolist() with col2: placeholder = st.empty() displayholder = st.empty() if st.button('Simulate appropriate pivots'): with placeholder: if site_var1 == 'Draftkings': working_roo = raw_baselines working_roo.replace('', 0, inplace=True) if site_var1 == 'Fanduel': working_roo = raw_baselines working_roo.replace('', 0, inplace=True) own_dict = dict(zip(working_roo.Player, working_roo.Own)) team_dict = dict(zip(working_roo.Player, working_roo.Team)) opp_dict = dict(zip(working_roo.Player, working_roo.Opp)) pos_dict = dict(zip(working_roo.Player, working_roo.Position)) total_sims = 1000 if check_seq == 'Single Player': player_var = working_roo.loc[working_roo['Player'] == player_check] player_var = player_var.reset_index() working_roo = working_roo[working_roo['Position'].isin(pos_var_list)] working_roo = working_roo[working_roo['Team'].isin(team_var1)] working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo['Salary'] <= player_var['Salary'][0] + Salary_var)] working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)] flex_file = working_roo[['Player', 'Position', 'Salary', 'Median']] flex_file['Floor_raw'] = flex_file['Median'] * .25 flex_file['Ceiling_raw'] = flex_file['Median'] * 2 flex_file['Floor'] = np.where(flex_file['Position'] == 'G', flex_file['Median'] * .5, flex_file['Floor_raw']) flex_file['Floor'] = np.where(flex_file['Position'] == 'D', flex_file['Median'] * .1, flex_file['Floor_raw']) flex_file['Ceiling'] = np.where(flex_file['Position'] == 'G', flex_file['Median'] * 1.75, flex_file['Ceiling_raw']) flex_file['Ceiling'] = np.where(flex_file['Position'] == 'D', flex_file['Median'] * 1.75, flex_file['Ceiling_raw']) flex_file['STD'] = flex_file['Median'] / 3 flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] hold_file = flex_file.copy() overall_file = flex_file.copy() salary_file = flex_file.copy() overall_players = overall_file[['Player']] for x in range(0,total_sims): salary_file[x] = salary_file['Salary'] overall_file[x] = random.normal(overall_file['Median'],overall_file['STD']) salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) salary_file = salary_file.div(1000) overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) players_only = hold_file[['Player']] raw_lineups_file = players_only for x in range(0,total_sims): maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))} raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])]) players_only[x] = raw_lineups_file[x].rank(ascending=False) players_only=players_only.drop(['Player'], axis=1) salary_2x_check = (overall_file - (salary_file*2)) salary_3x_check = (overall_file - (salary_file*3)) salary_4x_check = (overall_file - (salary_file*4)) players_only['Average_Rank'] = players_only.mean(axis=1) players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims) players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims) players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims) players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims) players_only['Player'] = hold_file[['Player']] final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] final_Proj = pd.merge(hold_file, final_outcomes, on="Player") final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] final_Proj['Own'] = final_Proj['Player'].map(own_dict) final_Proj['Team'] = final_Proj['Player'].map(team_dict) final_Proj['Opp'] = final_Proj['Player'].map(opp_dict) final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']] final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True) final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) final_Proj['LevX'] = 0 final_Proj['LevX'] = np.where(final_Proj['Position'] == 'C', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'W', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'D', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'G', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['CPT_Own'] = final_Proj['Own'] / 4 final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']] final_Proj = final_Proj.set_index('Player') st.session_state.final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False) elif check_seq == 'Top X Owned': if pos_var1 == 'Specific Positions': raw_baselines = raw_baselines[raw_baselines['Position'].isin(pos_var_list)] player_check = raw_baselines['Player'].head(top_x_var).tolist() final_proj_list = [] for players in player_check: players_pos = pos_dict[players] player_var = working_roo.loc[working_roo['Player'] == players] player_var = player_var.reset_index() working_roo_temp = working_roo[working_roo['Position'] == players_pos] working_roo_temp = working_roo_temp[working_roo_temp['Team'].isin(team_var1)] working_roo_temp = working_roo_temp.loc[(working_roo_temp['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo_temp['Salary'] <= player_var['Salary'][0] + Salary_var)] working_roo_temp = working_roo_temp.loc[(working_roo_temp['Median'] >= player_var['Median'][0] - Median_var) & (working_roo_temp['Median'] <= player_var['Median'][0] + Median_var)] flex_file = working_roo_temp[['Player', 'Position', 'Salary', 'Median']] flex_file['Floor_raw'] = flex_file['Median'] * .25 flex_file['Ceiling_raw'] = flex_file['Median'] * 2 flex_file['Floor'] = np.where(flex_file['Position'] == 'G', flex_file['Median'] * .5, flex_file['Floor_raw']) flex_file['Floor'] = np.where(flex_file['Position'] == 'D', flex_file['Median'] * .1, flex_file['Floor_raw']) flex_file['Ceiling'] = np.where(flex_file['Position'] == 'G', flex_file['Median'] * 1.75, flex_file['Ceiling_raw']) flex_file['Ceiling'] = np.where(flex_file['Position'] == 'D', flex_file['Median'] * 1.75, flex_file['Ceiling_raw']) flex_file['STD'] = flex_file['Median'] / 3 flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] hold_file = flex_file.copy() overall_file = flex_file.copy() salary_file = flex_file.copy() overall_players = overall_file[['Player']] for x in range(0,total_sims): salary_file[x] = salary_file['Salary'] overall_file[x] = random.normal(overall_file['Median'],overall_file['STD']) salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) salary_file = salary_file.div(1000) overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) players_only = hold_file[['Player']] raw_lineups_file = players_only for x in range(0,total_sims): maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))} raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])]) players_only[x] = raw_lineups_file[x].rank(ascending=False) players_only=players_only.drop(['Player'], axis=1) salary_2x_check = (overall_file - (salary_file*2)) salary_3x_check = (overall_file - (salary_file*3)) salary_4x_check = (overall_file - (salary_file*4)) players_only['Average_Rank'] = players_only.mean(axis=1) players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims) players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims) players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims) players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims) players_only['Player'] = hold_file[['Player']] final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] final_Proj = pd.merge(hold_file, final_outcomes, on="Player") final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] final_Proj['Own'] = final_Proj['Player'].map(own_dict) final_Proj['Team'] = final_Proj['Player'].map(team_dict) final_Proj['Opp'] = final_Proj['Player'].map(opp_dict) final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']] final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True) final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) final_Proj['LevX'] = 0 final_Proj['LevX'] = np.where(final_Proj['Position'] == 'C', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'W', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'D', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'G', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['CPT_Own'] = final_Proj['Own'] / 4 final_Proj['Pivot_source'] = players final_Proj = final_Proj[['Player', 'Pivot_source', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']] final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False) final_proj_list.append(final_Proj) st.write(f'finished run for {players}') # Concatenate all the final_Proj dataframes final_Proj_combined = pd.concat(final_proj_list) final_Proj_combined = final_Proj_combined.sort_values(by='LevX', ascending=False) final_Proj_combined = final_Proj_combined[final_Proj_combined['Player'] != final_Proj_combined['Pivot_source']] st.session_state.final_Proj = final_Proj_combined.reset_index(drop=True) # Assign the combined dataframe back to final_Proj placeholder.empty() with displayholder.container(): if 'final_Proj' in st.session_state: st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(st.session_state.final_Proj), file_name='NHL_pivot_export.csv', mime='text/csv', ) else: st.write("Run some pivots my dude/dudette") with tab2: st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'.") col1, col2 = st.columns([1, 5]) with col1: proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader') if proj_file is not None: try: proj_dataframe = pd.read_csv(proj_file) except: proj_dataframe = pd.read_excel(proj_file) with col2: if proj_file is not None: st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)